The textile industry creates one-fifth of the world's industrial water pollution, thus, the electrocoagulation (EC) process was proposed and investigated as an alternative eco-friendly treatment for water reuse. This study aimed to assess the removal efficiency of the Reactive Black 5 (RB5) from synthetic textile effluent by EC. Key operating parameters on EC process efficiency were optimized using response surface methodology (RSM). The main independent variables studied were the current density, EC time, concentration, and pH while the RB5 dye removal was studied as the dependent variable. The range of the studied parameters affecting the EC process ranged from 10 to 60 mA/cm2, 5 to 30 min, 4 to 10 pH and 10 to 40 ppm for current density, EC time, pH, and RB5 concentration, respectively. The optimal operating parameters turned out to be 5.5 pH, 47.5 mA/cm2, 23.75 min, 17.5 ppm, and the predicted RB5 dye removal was 96.33%. The experimental dye removal with optimum operating conditions was in good agreement with the predicted removal efficiency. Therefore, the experiment results revealed the high potential of the EC process to effectively treat textile industry effluents and the RB5 dye removal was successfully optimized using Response Surface Methodology (RSM).

  • The removal of Reactive Black 5 from simulated textile effluent by electrocoagulation process was investigated.

  • RB5 dye removal process was successfully optimized using RSM.

  • The experimental dye removal with optimum operating conditions was in good agreement with the predicted removal efficiency.

  • The experiment results proved that the electrocoagulation process is a potential eco-friendly process to treat textile industry effluents.

One of the major global challenges is to maintain water safety (Hakizimana et al. 2017; Dehghan et al. 2021). Worldwide population growth is estimated at 40% in 50 years to come and yet, sharp industrial growth has led to an increased freshwater demand. Various anthropogenic activities such as industries, deforestation, and urbanization result in freshwater contamination (Shaabani et al. 2018; Najid et al. 2022). Efficient management of freshwater resources along with wastewater treatment facilities for water reuse may constitute a mitigating solution to the growing freshwater demand. In fact, textile effluent is one of the most environmental pollutants, especially for water and soil contamination as well as the ecosystem (Khandegar & Saroha 2013; Kobya et al. 2016; Wanyonyi et al. 2019; Azanaw et al. 2022). The dye effluents in Figure 1 are among the main environmental vulnerability as they contaminate both surface and groundwater (Prabha et al. 2013; Wanyonyi et al. 2019). Also, the presence of salts in textile wastewater causes soil infertility and destroys aquatic life (Khandegar & Saroha 2013). The main challenges of textile processing are the consumption of a huge volume of water and the introduction of various chemicals in the effluents (Ahmadi & Amiri 2016; Wanyonyi et al. 2019). The presence of the color does not only change the aesthetic appearance of water, but it also limits light penetration into the water. It also slows down the photosynthetic activity in aquatic plant species. These become vulnerable and even possibly die (Şengil & Özacar 2008; Mohamadi et al. 2014; Meddah et al. 2021; Martins et al. 2023). On the other hand, the azo dyes are recognized as the highly used synthetic dyes by textile industries (Roriz et al. 2009; Salehi et al. 2016; Meddah et al. 2021; Ayed et al. 2022). Furthermore, azo dyes have been identified to account for approximately 70% of the most used in the industrial sector (Salehi et al. 2016). The reactive dyes are mainly applied due to their easiest binding on fibers. They are deeply colored dyes and even inexpensive (Mohamadi et al. 2014). It has been revealed that almost 15% of the dye is unreacted and released into the effluents. The azo dyes are less biodegradable compounds (Mook et al. 2017). The Reactive Black 5 (RB5) has been considered in this study due to a remarkably wide use by textile industries. It accounts for 50% of the demand compared to other reactive dyes across the globe (Mook et al. 2017; Naraghi et al. 2018).
Figure 1

Vulnerability effects of untreated dye effluents in the environment.

Figure 1

Vulnerability effects of untreated dye effluents in the environment.

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According to the literature, the EC process turned out to be very efficient for the abatement of a wide range of pollutants (Emamjomeh & Sivakumar 2009), thus, this process may act as an excellent technology for textile wastewater treatment. The dye removal from wastewater is a huge challenge for most conventional treatment (Zaroual et al. 2006; Kobya et al. 2016; Meddah et al. 2021). Some dyes are toxic and are not easily biodegraded by biological processes (Roriz et al. 2009; Kabdaşlı & Tünay 2012; Meddah et al. 2021). Chemical coagulation–flocculation can be used for dye removal, but it is ineffective for decolorizing textile effluents. Also, the addition of chemicals into water presents its drawback (Zaroual et al. 2006; Yousefi et al. 2022). EC has been considered for the treatment of dye streams from textile industries wastewater and it does not need adding chemicals for the process which makes it an eco-friendly process (Khandegar & Saroha 2014). The EC process combines physical and chemical mechanisms with many electrochemical phenomena involved.

The EC process occurs via electrolytic reactions at electrode surfaces. EC is an electrochemical process that combines electrodissolution, coagulation, and flocculation. Metal cations are electrogenerated in situ from the sacrificial anode under the action of an electric current applied between the electrodes. The amount of metal cations generated is governed by Faraday's law. These metal cations spontaneously undergo hydrolysis, leading thereby to the metal hydroxide species, depending on the pH of aqueous medium (Hakizimana et al. 2017). The hydrolyzed metals will act as coagulants and/or adsorbents and/or electrostatic attractor of pollutants for their removal. The oxidation reaction takes place at the anode and the reduction of water at the cathode (An et al. 2016). Thus, the respective electrochemical reactions are summarized as follows:
(1)
(2)
Textile dyes are soluble pollutants, and their possible abatement mechanisms by the EC process are summarized in Figure 2 (Hakizimana et al. 2017):
  • The purely physical enmeshment of dissolved substances during hydroxide precipitation, adsorption, and complexation.

  • The electro-oxidation on the anode or the electro-reduction on the cathode of electro-active ions or molecules.

  • The direct adsorption of pollutants on the electrodes.

Figure 2

Main mechanisms of soluble pollution abatement using EC.

Figure 2

Main mechanisms of soluble pollution abatement using EC.

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Figure 3

Molecular structure of Reactive Black 5 dye (Chang et al. 2010).

Figure 3

Molecular structure of Reactive Black 5 dye (Chang et al. 2010).

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As RB5 is the main azo dye widely used in the textile industry and the research on RB5 dye effluent treatment by the EC process is almost non-existent in literature, the current study aims at investigating and validating the potential applicability of the EC process as a mature technology to treat textile effluent for its reuse. The RB5 removal efficiency by the EC process is assessed in batch mode with aluminum electrodes and operating parameters are optimized using surface response methodology. Interaction between the different independent factors such as the current density, EC time, concentration, and initial pH and their influence on the dependent factor which is the RB5 dye removal efficiency are assessed.

Experimental set up and procedure

The commercial RB5 dye the commercial RB5 dye powder (Figure 3) obtained from Sigma Aldrich Co. (USA) was used. The solutions of the RB5 have been prepared by dissolving the RB5 dye in tap water (Chafi et al. 2011; Merzouk et al. 2013). The cylindrical batch reactor was used and the full experimental setup is shown in Figure 4. The conductivity of solutions was adjusted to 5.58 mS/cm for all solution runs by adding NaCl. The conductivity and pH were measured by a multiprobe meter (HACH HQ40d). The pH of the solution was adjusted by adding 0.5 M of NaOH or 0.5 M of HCl droplets. The EC process was carried out in a cylindrical batch reactor with a volume of 400 ml of which 250 ml were active. The EC cell was constructed using two aluminum parallel plates. Both of the aluminum plates served as the anode and cathode. The electrodes were used with an active surface area of 20 cm2 and the distance between the two electrodes in all experiment was kept at 2.5 cm. In order to remove the oxide protective layer of electrodes, before each run, both electrodes were immersed in 0.05 M H2SO4 solution for 2 min, rinsed with tap water and immersed in tap water for 4 min before placing them the EC batch cell. The direct current (DC) was supplied by a single-output adjustable DC power supply (M10-SP-303A). It had the galvanostatic operational options to adjust the current density. Filtration of samples after a desired treatment time was done with a micro-filter of 0.45 μm and absorbance at 597 nm was measured for each sample using a UV/Vis spectrophotometer (CECIL CE 2041, 2000 Series, Cambridge, England). The Faradaic yield of the electrodissolution is deduced from the ratio of experimental mass over theoretical mass calculated from Faraday's law. To deduce the theoretical mass, only the anode was weighted before use at the beginning and at the end of EC to obtain a difference in mass loss. The percent dye removal (%DR) is given by:
(3)
where Ao and A are absorbances before and after EC, respectively. It is determined based on Beer–Lambert law, which is:
(4)
where ε is molar absorptivity, l is length of light path, C is the concentration of the RB5 dye solution.
Figure 4

EC reactor setup: (a) DC currents supplier, (b) EC reactor, (c) electrodes (anode and cathode), and (d) magnetic stirrer).

Figure 4

EC reactor setup: (a) DC currents supplier, (b) EC reactor, (c) electrodes (anode and cathode), and (d) magnetic stirrer).

Close modal

The mass of the floc formed is proportional to the mass of aluminum released by electrodes and it changes over time (Daneshvar et al. 2007). This is clearly proven by Faraday's law using the Faradaic yield constant. It is calculated by comparing the experimental mass loss of aluminum electrodes during the EC process with the theoretical amount of aluminum dissolution, based on Faraday's law.

Statistical analysis and design of experiment

The response surface methodology (RSM) was used to analyze the effects of selected variables for the EC process. In fact, the RSM is a useful statistical tool for the optimization of various factors and for the design of experiments (DOE) (Nair et al. 2014). The second-order response surface model was used to optimize the operating conditions. The central composite design (CCD) was used. Therefore, the second-order polynomial equation is expressed as follows:
(5)
where y is the response, bo a constant, k the number of variables, bi the coefficients of the linear parameters, ε the residual expressed as the difference between the calculated and experimental results, xi and xj are variables, bii the coefficient of quadratic parameters and the coefficient of the interacting parameters. In fact, the response surface second-order model using CCD was used for designing the experiment. The CCD consists of three types of points involving cube points that originate from factorial design , axial points and central points . Number of experimental runs was clearly given by the expression:
(6)
where k is the number of factors. According to the Equation (6) we have , for this study that reflects 16 cube points and results in eight axial points. The center point in the cube is 7. Therefore, the sum of the cube, axial and central point results in 31 experiments to be studied. Each independent variable was coded into five levels (-α, −1, 0, + 1, +α). The code value (Xi) was obtained according to the following equation:
(7)
where,
(7a)
and
(7b)
xi represents the real value of factor i, xh high value of factor, xl low value of factor, xav represents the mean of high and low values of factor i, Δx represents the value of change. 0 is the midpoint, −1 is the minimum level, +1 is the maximum level, −α and +α are additional levels outside the minimum and maximum range of the variables. Where α is obtained by applying the following equation:
(8)

It means that is obtained due to the four (4) factors used for the study. Design Expert 13 software was used for designing the experiment and analysis of data.

RSM results

The most EC response influential independent factors were chosen as shown in Table 1 with their corresponding coded levels. The four parameters were used in coded form based on minimum, maximum, and points between minimum and maximum. The factors with their respective alpha () and () values are: Current density (10–60 mA/cm2), EC time (5–30 min), concentration (10–40 ppm), and pH (4–10). Therefore, 31 experimental runs with corresponding experimental and predicted dye percent removal are presented in Table 2.

Table 1

Independent variables and coded factors level

ParametersCodeCoded factors level
− 2− 1012
Current density (mA/cm210 22.5 35 47.5 60 
EC time (min) 11.25 17.5 23.75 30 
Concentration (ppm) 10 17.5 25 32.5 40 
pH 5.5 8.5 10 
ParametersCodeCoded factors level
− 2− 1012
Current density (mA/cm210 22.5 35 47.5 60 
EC time (min) 11.25 17.5 23.75 30 
Concentration (ppm) 10 17.5 25 32.5 40 
pH 5.5 8.5 10 
Table 2

RB5 dye removal percent and CCD in response surface methodology

Run orderA (current density [mA/Cm2])B (electrolysis time[min])C (concentration [ppm])D (pH)% Actual RB5% Predicted RB5
0 (35) 0 (17.5) 0 (25) 0 (7) 61.84 65.28 
0 (35) 0 (17.5) 0 (25) −2 (4) 65.09 63.20 
2 (60) 0 (17.5) 0 (25) 0 (7) 95.20 86.49 
0 (35) 0 (17.5) 0 (25) 2 (10) 35.40 41.97 
0 (35) 0 (17.5) 0 (25) 0 (7) 64.62 65.28 
1 (47.5) −1 (11.25) −1 (17.5) 1 (8.5) 64.16 56.48 
0 (35) 0 (17.5) −2 (10) 0 (7) 64.67 72.66 
−1 (22.5) 1 (23.75) −1 (17.5) −1 (5.5) 71.02 68.74 
−1 (22.5) −1 (11.25) 1 (32.5) 1 (8.5) 35.27 34.27 
10 −1 (22.5) −1 (11.25) −1 (17.5) −1 (5.5) 53.07 52.26 
11 0 (35) 2 (30) 0 (25) 0 (7) 93.01 88.14 
12 1 (47.5) 1 (23.75) 1 (32.5) 1 (8.5) 78.16 78.33 
13 0 (35) −2 (5) 0 (25) 0 (7) 38.33 42.42 
14 0 (35) 0 (17.5) 0 (25) 0 (7) 62.86 65.28 
15 1 (47.5) 1 (23.75) −1 (17.5) −1 (5.5) 94.23 96.33 
16 0 (35) 0 (17.5) 0 (25) 0 (7) 63.56 65.28 
17 0 (35) 0 (17.5) 0 (25) 0 (7) 65.02 65.28 
18 −1 (22.5) −1 (11.25) −1 (17.5) 1 (8.5) 49.63 41.65 
19 −1 (22.5) 1 (23.75) 1 (32.5) −1 (5.5) 59.65 61.35 
20 0 (35) 0 (17.5) 0 (25) 0 (7) 64.92 65.28 
21 1 (47.5) −1 (11.25) 1 (32.5) 1 (8.5) 43.55 49.09 
22 1 (47.5) −1 (11.25) 1 (32.5) −1 (5.5) 61.11 59.70 
23 0 (35) 0 (17.5) 0 (25) 0 (7) 62.54 65.28 
24 1 (47.5) −1 (11.25) −1 (17.5) −1 (5.5) 64.82 67.09 
25 −2 (10) 0 (17.5) 0 (25) 0 (7) 37.02 44.07 
26 0 (35) 0 (17.5) 2 (40) 0 (7) 61.00 57.90 
27 −1 (22.5) 1 (23.75) 1 (32.5) 1 (8.5) 51.54 50.74 
28 1 (47.5) 1 (23.75) −1 (17.5) 1 (8.5) 85.10 85.72 
29 −1 (22.5) 1 (23.75) −1 (17.5) 1 (8.5) 64.83 58.13 
30 −1 (22.5) −1 (11.25) 1 (32.5) −1 (5.5) 52.15 44.88 
31 1 (47.5) 1 (23.75) 1 (32.5) −1 (5.5) 84.14 88.95 
Run orderA (current density [mA/Cm2])B (electrolysis time[min])C (concentration [ppm])D (pH)% Actual RB5% Predicted RB5
0 (35) 0 (17.5) 0 (25) 0 (7) 61.84 65.28 
0 (35) 0 (17.5) 0 (25) −2 (4) 65.09 63.20 
2 (60) 0 (17.5) 0 (25) 0 (7) 95.20 86.49 
0 (35) 0 (17.5) 0 (25) 2 (10) 35.40 41.97 
0 (35) 0 (17.5) 0 (25) 0 (7) 64.62 65.28 
1 (47.5) −1 (11.25) −1 (17.5) 1 (8.5) 64.16 56.48 
0 (35) 0 (17.5) −2 (10) 0 (7) 64.67 72.66 
−1 (22.5) 1 (23.75) −1 (17.5) −1 (5.5) 71.02 68.74 
−1 (22.5) −1 (11.25) 1 (32.5) 1 (8.5) 35.27 34.27 
10 −1 (22.5) −1 (11.25) −1 (17.5) −1 (5.5) 53.07 52.26 
11 0 (35) 2 (30) 0 (25) 0 (7) 93.01 88.14 
12 1 (47.5) 1 (23.75) 1 (32.5) 1 (8.5) 78.16 78.33 
13 0 (35) −2 (5) 0 (25) 0 (7) 38.33 42.42 
14 0 (35) 0 (17.5) 0 (25) 0 (7) 62.86 65.28 
15 1 (47.5) 1 (23.75) −1 (17.5) −1 (5.5) 94.23 96.33 
16 0 (35) 0 (17.5) 0 (25) 0 (7) 63.56 65.28 
17 0 (35) 0 (17.5) 0 (25) 0 (7) 65.02 65.28 
18 −1 (22.5) −1 (11.25) −1 (17.5) 1 (8.5) 49.63 41.65 
19 −1 (22.5) 1 (23.75) 1 (32.5) −1 (5.5) 59.65 61.35 
20 0 (35) 0 (17.5) 0 (25) 0 (7) 64.92 65.28 
21 1 (47.5) −1 (11.25) 1 (32.5) 1 (8.5) 43.55 49.09 
22 1 (47.5) −1 (11.25) 1 (32.5) −1 (5.5) 61.11 59.70 
23 0 (35) 0 (17.5) 0 (25) 0 (7) 62.54 65.28 
24 1 (47.5) −1 (11.25) −1 (17.5) −1 (5.5) 64.82 67.09 
25 −2 (10) 0 (17.5) 0 (25) 0 (7) 37.02 44.07 
26 0 (35) 0 (17.5) 2 (40) 0 (7) 61.00 57.90 
27 −1 (22.5) 1 (23.75) 1 (32.5) 1 (8.5) 51.54 50.74 
28 1 (47.5) 1 (23.75) −1 (17.5) 1 (8.5) 85.10 85.72 
29 −1 (22.5) 1 (23.75) −1 (17.5) 1 (8.5) 64.83 58.13 
30 −1 (22.5) −1 (11.25) 1 (32.5) −1 (5.5) 52.15 44.88 
31 1 (47.5) 1 (23.75) 1 (32.5) −1 (5.5) 84.14 88.95 

The experimental results were analyzed through CCD in RSM to obtain a response. The actual RB5 percentage (%RB5) removal represented the measured response data for a given experimental run while the predicted RB5 percentage (%RB5) removal was deduced from the second polynomial model generated from RSM.

Analysis of variance (ANOVA)

The regression equation (second-order polynomial) model was obtained as the relationship between factors and response. The equation is expressed as follows:

(9)
where %RB5 is the dye removal efficiency.
The analysis of variance, p-value in Figure 5 presents the coefficients at 95% confidence level, where AA, BB, CC, AC, AD, BC, BD, and CD were statistically insignificant, and then it was removed from Equation (9). The new second polynomial model Equation (10) became the following.
(10)
Figure 5

Estimated p-values of variables for RB5 dye removal efficiency.

Figure 5

Estimated p-values of variables for RB5 dye removal efficiency.

Close modal

The results of the analysis of variance for the reduced model in Table 3 were established with new regression model Equation (10) with the following parameters: EC time, current density, concentration and pH. All of them are significant in their linear terms. The p-value acts as the reference to ensure the significance of each parameter (Razieh et al. 2020; Meddah et al. 2021). Due to that, p-value was estimated at a 5% level, by considering the results obtained at linearity are even significant at a 1% level. The pH quadratic factor, the current density and EC time interaction factor have shown the p-values of 0.0023 and 0.018, which is less than p-value of 0.05. They are significant at a 95% confidence level. The R2 value of 92.26%, the adjusted R2 (R2adj) of 90.33% and the predicted R2 (R2pred) of 84.75% have shown the values that are closer and they present a satisfactory adjustment of the quadratic model to the experimental results. The difference between R2pred and R2adj should be less than 0.20 in order to confirm that the results or model are reliable (Mook et al. 2017; Razieh et al. 2020). Therefore, R2pred is in reasonable agreement with the R2adj which indicates the validity and suitability of the obtained model to fit the RB5 removal efficiency data.

Table 3

ANOVA for reduced quadratic model (model summary; R2 = 92.26%, RR2adj = 90.33%, R2pred = 84.75%)

SourceSum of squaresdfMean squareF-valuep-value
Model 7,295.74 1,215.96 47.69 < 0.0001 
A – current density 2,698.61 2,698.61 105.84 < 0.0001 
B – EC time 3,135.00 3,135.00 122.95 < 0.0001 
C – concentration 327.12 327.12 12.83 0.0015 
D – pH 675.73 675.73 26.50 < 0.0001 
AB 162.99 162.99 6.39 0.0185 
D² 296.29 296.29 11.62 0.0023 
Residual 611.94 24 25.50   
Lack of fit 602.36 18 33.46 20.96 0.0006 
Pure error 9.58 1.60   
Cor. total 7,907.68 30    
SourceSum of squaresdfMean squareF-valuep-value
Model 7,295.74 1,215.96 47.69 < 0.0001 
A – current density 2,698.61 2,698.61 105.84 < 0.0001 
B – EC time 3,135.00 3,135.00 122.95 < 0.0001 
C – concentration 327.12 327.12 12.83 0.0015 
D – pH 675.73 675.73 26.50 < 0.0001 
AB 162.99 162.99 6.39 0.0185 
D² 296.29 296.29 11.62 0.0023 
Residual 611.94 24 25.50   
Lack of fit 602.36 18 33.46 20.96 0.0006 
Pure error 9.58 1.60   
Cor. total 7,907.68 30    

The predicted versus actual plot of RB5 dye removal in Figure 6, with its regression line proves the reliability and accuracy of the regression model in Equation (10). It represents the closeness between the predicted and experimental results.
Figure 6

Plot of predicted values versus actual values for RB5 dye removal efficiency.

Figure 6

Plot of predicted values versus actual values for RB5 dye removal efficiency.

Close modal
The justification of the normality assumption was established by ensuring the normal plot of residual. The normality assumption is satisfied if the points of the plot approximately follow a straight line (Roriz et al. 2009). The residuals versus predicted values and normal probability for RB5 dye removal are shown in Figure 7. The fulfillment of the normality assumption in Figure 7(b) shows clearly that the points in the plot follow a straight line. The plot of residuals versus predicted in Figure 7(a) proves the robustness of the model. The points are distributed without an increase or decrease. However, it illustrates the increase of residuals with fits and even both positive and negative sides show a predominant point. Therefore, Figure 7 proves that the regression equation second-order polynomial model obtained from the RSM satisfactorily predicts the RB5 dye removal efficiency by EC process.
Figure 7

(a) Residual versus predicted plot and (b) normal probability plot.

Figure 7

(a) Residual versus predicted plot and (b) normal probability plot.

Close modal

Effect of factors interaction on RB5 dye removal

The surface plots with three dimensions were established using a Regression equation second-order polynomial model generated from RSM. They show the effect of each factor on the response (Shah et al. 2017). As for the surface plots shown in Figure 8, two factors are varied according to the designed experiment and the other two are fixed at zero level, the value that corresponds to midpoints. The experimental levels and the value of each factor were enhanced and the relationship with response. As a result, the surface plots help to know the impact of each of the two studied factors and their interaction on the response as well as to analyze the optimum condition of each parameter to ensure the RB5 efficient removal.
Figure 8

3D surface plots of RB5 removal efficiency: (a) effect of current density and EC time; (b) effect of current density and concentration; (c) effect of current density and pH; (d) effect of EC time and concentration; (e) effect of EC time and pH; (f) effect of concentration and pH.

Figure 8

3D surface plots of RB5 removal efficiency: (a) effect of current density and EC time; (b) effect of current density and concentration; (c) effect of current density and pH; (d) effect of EC time and concentration; (e) effect of EC time and pH; (f) effect of concentration and pH.

Close modal

Interaction between current density and EC time

The EC time and the current density interaction as shown in Figure 8(a) demonstrated a great influence on RB5 dye removal efficiency. The latter increased with increasing time and current density while initial concentration and pH were set constant at midpoints 25 ppm and 7, respectively. From the current density of 47.5 mA/cm2 and treatment time of 23.75 min to 60 mA/cm2 and 30 min, the surface plot predicted the RB5 removal efficiency that increased from 80 to 100%, respectively. An increase in current density and EC time resulted in an increasing RB5 removal efficiency. The current density which is defined as the current over the active electrode surface area determines the amount of coagulant electrochemically released in the EC reactor and EC (electrolysis) time in return increases the amount of coagulant according to Faraday's law. Thus, increasing the current density and EC time at the same time results in increasing considerably the RB5 removal efficiency (Kobya et al. 2016; Hakizimana et al. 2017). Furthermore, on the one hand, the current density is also known to control the production and size of hydrogen bubbles at the cathode which can enhance the mass transfer between hydroxide anions and metal cations resulting in the efficient formation of aluminum hydroxides (coagulant) (Daneshvar et al. 2006). On the other hand, these hydrogen bubbles produced at the cathodes encounter the flocs that are attached to the bubble surface which brings them to the top of the EC reactor. This constituted the main liquid-solid phase separation associated with the EC, known as the electroflotation process (Hakizimana et al. 2017). The EC time increased the amount of coagulant and enhanced the contact between the coagulant and the RB5 dye which resulted in increasing RB5 dye removal efficiency (Mook et al. 2017).

Interaction between current density and RB5 dye concentration

The effect of current density combined with that of RB5 concentration is shown in Figure 8(b). A better RB5 removal is obtained at high current density with slightly low concentration. At the current density of 23.75 min and concentration of 17.5 ppm the RB5 removal efficiency has been expected to reach between 70 and 80%. The lowest percentage was observed at high concentration and low current density with 40% removal. The increase of concentration requires a huge amount of the coagulant in the EC reactor, which involves increasing the current density. Increasing the current density augments the metal cations, hydroxyl anions and hydrogen bubbles electrochemically produced during the EC process. The efficient formation of Al(OH)3 enhances a large surface area to remove the dye (Mollah et al. 2010) and considerable hydrogen bubbles production increases the contact between Al(OH)3 coagulants and RB5 dye. Thus, the removal efficiency of RB5 increases with decreasing moderate concentration and increasing current density (Mook et al. 2017).

Interaction between pH and current density

Current density is the key parameter affecting the EC process by determining the amount of metal cations generated according to Faraday's law. pH is also an important factor influencing EC efficiency through the pollutant removal mechanisms as it governs the hydrolysis of metal species generated from electrodissolution in an aqueous medium according to the pourbaix diagram (Hakizimana et al. 2017). Depending on the initial pH, hydrolysis of aluminum cations Al3+ generated from the electrodissolution of the anode leads to the predominance of Al hydroxides positively charged in the acidic pH range, such as Al(OH)2+ and Al(OH)2+, to the predominance of insoluble Al hydroxide Al(OH)3 at neutral pH and to the predominance of aluminate anions Al(OH)4 in the alkaline pH range (Hakizimana et al. 2017). The 3D plot of the interaction between the pH and the current density on EC (Figure 8(c)) displays a high RB5 removal efficiency, in the pH range of 5.5 to 7 regardless of the current density and RB5 removal efficiency in the alkaline medium. Alongside hydrolyzed aluminum species, the RB5 classified as anionic dye in aqueous solution exists in dissociated form as anionic dye ions (Venkata et al. 2018):
(11)
Taking into account the negative charge of the anionic RB5 dye in an aqueous solution, and the charge of dependent coagulant species as a function of pH range, the RB5 dye removal efficiency may be deduced from electrostatic interactions. In acidic conditions, there is efficient co-precipitation of Al(OH)2+ and Al(OH)2+ with anionic RB5 and efficient adsorption of anionic RB5 on insoluble Al(OH)3 and the adsorbent, because a significantly high electrostatic force of attraction exists between the positively charged surfaces of the aluminum hydrolyzed species and the negative charged anionic dye. Insoluble aluminum hydroxides Al(OH)3 in the neutral pH act as a moderate dye adsorbent to the anionic RB5. Soluble aluminum hydroxides in alkaline solutions of Al(OH)4 turn out to be inefficient for RB5 dye removal. The low adsorption capacity under alkaline conditions could be mainly attributed to the increasing number of negative charges on the surface of the adsorbent surface could result in electrostatic repulsion between the adsorbent and dye molecules (Venkata et al. 2018; Hashim et al. 2019). Based on the main mechanisms of soluble pollution abatement using EC as summarized in Figure 2, and on the results obtained, the main mechanisms taking place between the pH-dependent coagulant species and their neighboring RB5 dye turn to be co-precipitation, adsorption and sweep flocculation (physical enmeshment). This has been confirmed in the literature for the removal of dyes at a specified pH range by EC (Rebhun & Lurie 1993; Kobya et al. 2016).
(12)
(13)
(14)
(15)
The Al(OH)3 species contain a large surface area, as a consequence they should trap a considerable amount of RB5 dye through sweep flocculation (physical enmeshment). However, given the neutral charge of Al(OH)3 and the negative charge of anionic RB5 in an aqueous solution, the dye removal efficiency in neutral pH turned out to be moderate. In neutral pH, the adsorption mechanism may end up with sweep flocs that are formed and even polymerized (Rebhun & Lurie 1993; Mollah et al. 2010) as shown in the following Equation (16).
(16)

Interaction between RB5 concentration and EC time

The high concentration and the short EC time in Figure 8(d) shows that there is a remarkable decrease in RB5 removal efficiency. It is reported by Chang et al. (2010), that the increase in concentration requires a considerable amount of coagulant in the EC process, which entails EC long time at constant current density. In Figure 8(d), EC time set at 23.75 min and the concentration within the range between 13.75–17.5 ppm, the RB5 removal efficiency reached between 80 and 90%. Therefore, a highly concentrated dye requires an electrochemical dissolution that needs to be generated for a long time to trap the dye. The slightly low concentration with the long electrolysis time led to the high RB5 removal efficiency.

Interaction between pH and EC time

The Initial pH and the EC time in Figure 8(e) influenced greatly the performance of the EC process. Between pH of 4 to 7.6, the RB5 removal efficiency increased from 25% at 5 min to 90% at 30 min. The results obtained revealed that the interaction with the best efficient RB5 removal occurred in acidic to neutral pH. It has been proved by Hashim et al. (2019), that within the acidic to neutral pH, the predominant species is Al(OH)3. The Al(OH)3 species has a huge surface area for efficient dye removal through sweep coagulation and precipitation mechanisms (Kobya et al. 2016). On the other hand, the pH beyond 7 showed a decrease in the RB5 dye removal due to the predominant monomeric species Al(OH)4 . The different forms of charged multimeric hydroxo Al3+ species may be formed as time of treatment increases. These hydroxo cationic complexes are charged efficiently to remove the pollutants by adsorption to produce the charge neutralization, and by enmeshment in a precipitate (Mollah et al. 2001).

Interaction between pH and RB5 concentration

The concentration and the pH interaction in Figure 8(f) impacted the RB5 removal efficiency. The RB5 dye removal efficiency was slightly affected by the RB5 concentration while the former was considerably affected by pH with high removal in the pH range of 4 to 7. The highest RB5 removal efficiency of 70% was obtained with a low RB5 concentration (13.75 ppm) and with a pH range of 4 to 7. The lowest RB5 removal efficiency was obtained with a high RB5 concentration (13.75 ppm) and with a pH of 10. The effect of pH on RB5 dye removal has been previously explained in detail. The slight decrease in RB5 dye removal efficiency at the same current density is due to a high amount of pollutant with the same amount of aluminum cations electrochemically generated in the EC reactor as the current was kept constant at the midpoint.

Optimization

The Design Expert software has been used as the key solution for predicting the maximum response to ensure the optimum conditions for RB5 dye removal. The plot in Figure 9 demonstrates clearly the influence of each variable to provide a response. In fact, the coded values displayed in Figure 9 show the levels of actual factors. The red cross represents the setting of the parameters to obtain the optimum response.
Figure 9

Response optimization of RB5 dye removal efficiency.

Figure 9

Response optimization of RB5 dye removal efficiency.

Close modal

Therefore, the dye removal percentage with predicted factors are current density 1(47.5 mA/cm2), EC time 1(23.75 min), initial concentration − 1(17.5 ppm) and pH − 1(5.5). The maximum predicted dye removal percentage with a desirability value of 1, has reached 96.33%. The reliability of the predicting model was verified through the experimental run with the deduced optimum conditions and the removal efficiency predicted was in good agreement with the experimental results with 94.23% as the RB5 removal efficiency. Therefore, the regression polynomial Equation (14) turned out to accurately model the RB5 dye removal efficiency by EC.

Aluminum dissolution

EC process removes the pollutants by dissolving the electrode in solution. Chemical and electrochemical dissolution may occur simultaneously on the anode and cathode (Cañizares et al. 2005; Mansouri et al. 2011). Electrode dissolution is a key parameter especially when technical economics is involved because it allows to determine the mass of the electrode dissolved and thereafter to deduce the operating cost related to electrodes. Operating cost noted OP expressed in $/m3 of treated water considers two major cost items namely, the specific electrical energy consumption (SEEC) and the electrode material consumption. SEEC is defined as the electric energy consumed during the treatment of a given volume of treated water while electrode material consumption corresponds to the experimental total aluminum concentration Cexp, or ϕ Cth, as described below:
(17)
where a ($/kWh) is the electrical energy unit price per kWh, b ($/kg) is the electrode material unit price, and ϕ the Faradaic yield.
In practice, instead of measuring the experimental aluminum consumed from electrodes, for every run it is recommended to deduce the Faradaic yield from the experimental results and theoretical data using Faraday's law as follows (Zodi et al. 2013):
(18)
(19)
where mexp refers to experimental mass, mth theoretical mass, t EC time, M molar mass.
The percentage of aluminum dissolution, which is defined as Faradaic efficiency may vary between 1 (100%) and 2 (200%) depending on the chemicals present in water and its pH (Hakizimana et al. 2017). The results in Figure 10 revealed that electrode dissociation for aluminum is around 1.15 (115%). Alongside the electrochemical anode dissolution (Equation (1)) supposed to give 100% from Faraday's law, the extra dissolution comes to the chemical dissolution as described by Equations (20) and (21), in addition, the presence of chloride ions may corrode aluminum electrodes (Cañizares et al. 2005; Mansouri et al. 2011; Hakizimana et al. 2015).
(20)
(21)
Figure 10

Aluminum anode electrode dissolution.

Figure 10

Aluminum anode electrode dissolution.

Close modal

In this study, the EC process was investigated as an eco-friendly technology to treat textile effluents containing RB5 dye. The research focused on the optimization of operating parameters affecting the EC process for the RB5 dye removal by using CCD in RSM from which the regression model was generated to predict EC for the RB5 dye percent removal. Two factors were varied according to the designed experiment and the other two were fixed at zero level, the value that corresponds to midpoints. The operating parameters that were analyzed and optimized are current density, EC time, RB5 dye concentration and pH. The RB5 dye removal increased with increasing current density, increasing EC time and with intermediate or low RB5 dye concentration in weak acid to neutral pH. The optimum operating conditions that were obtained were a pH of 5.5, the current density of 47.5 mA/cm2, an EC time of 23.75 min, and a concentration of 17.5 ppm. The predicted optimum RB5 dye removal was found to be 96.33% and the experimental RB5 dye removal efficiency of 94.23% was obtained with the predicted operating parameters. Thus, the EC process turned out to be an efficient process for RB5 dye removal from wastewater. In addition, the RSM enabled us to optimize the key parameters that affect the EC process where the values from the predictive regression model fit with the experimental values. Therefore, the optimized EC process constitutes an efficient potential technology for textile effluent treatment removal. For further study, the data from EC discontinuous mode should be extrapolated to continuous mode and the techno-economic evaluation should be conducted.

This work was supported by ISP fund from the SIDA program (Swedish International Development Cooperation Agency) to the University of Rwanda, College of Science and Technology.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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Supplementary data